The present invention is directed to the field of signal processing and signature and feature detection, particularly in images and image like data sets and higher dimensional data sets with related organizational structure. In particular, it is directed to recovering objects or features in data sets that have been generated by physics based computational simulation, and to the tracking of dynamically generated features in data sets. It is also directed to machine discovery and learning of associations in such data sets for, among other things, (1) relating design or control parameters to their effects in generating, or manipulating specifics of detail features of the resulting physics, or for (2) discovering relationships between features, multi-scale, or distributed characteristics of the physics and various macroscopic observables of the system.
Various applications are found in fluid flow simulations (computational fluid dynamics), experimental data sets and others relating to migratory media, wherein one may observe features such as (without limitation) shocks or vortices generated by flow around a particular shape, and one wishes to (a) discover and locate such features in a massive data set, (b) track the motion and change of scale of such features, (c) relate the generation and specific characteristics of such features to the geometry of the body around which fluid flows, (d) relate the generation and specific characteristics of such features to the nature or characteristics of the flowing fluid, (e) relate the generation and specific characteristics of such features to macroscopic parameters such as the lift and drag created on the subject body, or (f) render visual representations of such features within a flow.